Neural Models and Machine Translation

Übersicht

This course guides you through the core concepts behind neural language models and machine translation, focusing on how RNNs, attention, and transformers enable powerful NLP applications used in today’s AI systems. Through hands-on exercises, you’ll learn to build, fine-tune, and evaluate neural models for contextual language understanding, sentiment classification, and multilingual translation across various domains.

By the end of this course, you will be able to:

- Explain and implement core neural architectures, including RNNs, LSTMs, GRUs, and Transformers - Apply encoder-decoder frameworks and attention mechanisms to build translation systems - Fine-tune pretrained models like BERT, RoBERTa, and MarianMT for contextual NLP tasks - Address challenges such as domain adaptation, low-resource translation, and error correction - Evaluate model performance using BLEU, ROUGE, and semantic similarity metrics This course is ideal for NLP practitioners, machine learning engineers, and researchers aiming to build high-performing neural NLP systems for translation, classification, and conversational AI. A working knowledge of Python, NLP concepts, and machine learning is recommended.

Join us to master the neural foundations driving next-generation language understanding and generation.

Lehrplan

• Neural Language Models
Explore the foundations of neural networks in NLP, from word embeddings and RNNs to the powerful Transformer architecture. Learn how pretraining and fine-tuning power today’s intelligent systems through theory and hands-on demonstrations.
• Machine Translation (MT)
Understand the evolution of machine translation from rule-based systems to cutting-edge neural and transformer-based models. Dive into multilingual strategies, error handling, and domain adaptation for real-world translation challenges.
• Speech and Multimodal NLP
Discover how speech and multimodal data shape modern NLP. This module covers speech-to-text, TTS, and the integration of vision and audio with text for richer AI applications, alongside key trends like real-time NLP and model efficiency.
• Building Chatbots
Learn how to build intelligent chatbots using NLP techniques. This module covers intent detection, entity extraction, contextual fine-tuning, and performance evaluation, preparing you to design chatbots that integrate seamlessly into business workflows.
• Course Wrap-up and Assessments
Conclude the course by reviewing key concepts across neural models and machine translation. This module includes a graded knowledge check, a comprehensive course summary, and a project focused on building a smart multilingual assistant for global applications.

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Unterrichtet von

Edureka

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Fachgebiete

Computer Science